Binary Particle Swarm Optimization Algorithm with Mutation for Multiple Sequence Alignment

被引:0
作者
Long, Hai-Xia [1 ]
Xu, Wen-Bo [1 ]
Sun, Jun [1 ]
机构
[1] Jiangnan Univ, Sch Informat Technol, Wuxi 214122, Jiangsu, Peoples R China
来源
RIVISTA DI BIOLOGIA-BIOLOGY FORUM | 2009年 / 102卷 / 01期
关键词
Multiple sequence alignment; Binary particle swarm optimization; Mutation; Nucleic acid; Amino acids; HIDDEN MARKOV-MODELS; GENETIC ALGORITHM;
D O I
暂无
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Multiple sequence alignment (MSA) is a fundamental and challenging problem in the analysis of biologic sequence. The MSA problem is hard to be solved directly, for it always results in exponential complexity with the scale of the problem. In this paper, we propose mutation-based binary particle swarm optimization (M-BPSO) for MSA solving. In the proposed M-BPSO algorithm, BPSO algorithm is conducted to provide alignments. Thereafter, mutation operator is performed to move out of local optima and speed up convergence. From simulation results of nucleic acid and amino acid sequences, it is shown that the proposed M-BPSO algorithm has superior performance when compared to other existing algorithms. Furthermore, this algorithm can be used quickly and efficiently for smaller and medium size sequences.
引用
收藏
页码:75 / 94
页数:20
相关论文
共 50 条
  • [1] A multiple sequence alignment algorithm based on inertia weights particle swarm optimization
    Gao, Yuxi
    Journal of Bionanoscience, 2014, 8 (05): : 400 - 404
  • [2] A Method for Multiple Sequence Alignment Based on Particle Swarm Optimization
    Xu, Fasheng
    Chen, Yuehui
    EMERGING INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE, 2009, 5755 : 965 - +
  • [3] A study on inertia weight schemes with modified particle swarm optimization algorithm for multiple sequence alignment
    Lalwani, Soniya
    Kumar, Rajesh
    Gupta, Nilama
    2013 SIXTH INTERNATIONAL CONFERENCE ON CONTEMPORARY COMPUTING (IC3), 2013, : 283 - 288
  • [4] Multiobjective artificial fish swarm algorithm for multiple sequence alignment
    Dabba, Ali
    Tari, Abdelkamel
    Zouache, Djaafar
    INFOR, 2020, 58 (01) : 38 - 59
  • [5] Prediction of multiple sequence alignment based on an improved particle swarm optimization
    Gao, Y.-X., 1600, American Scientific Publishers (07): : 92 - 96
  • [6] Multiple Sequence Alignment with Hidden Markov Models Learned by Random Drift Particle Swarm Optimization
    Sun, Jun
    Palade, Vasile
    Wu, Xiaojun
    Fang, Wei
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2014, 11 (01) : 243 - 257
  • [7] Multiple sequence alignment using modified dynamic programming and particle swarm optimization
    Juang, Wang-Sheng
    Su, Shun-Feng
    JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS, 2008, 31 (04) : 659 - 673
  • [8] Improved Hidden Markov Model training for multiple sequence alignment by a particle swarm optimization - evolutionary algorithm hybrid
    Rasmussen, TK
    Krink, T
    BIOSYSTEMS, 2003, 72 (1-2) : 5 - 17
  • [9] ProbPFP: A Multiple Sequence Alignment Algorithm Combining Partition Function and Hidden Markov Model with Particle Swarm Optimization
    Zhan, Qing
    Wang, Nan
    Jin, Shuilin
    Tan, Renjie
    Jiang, Qinghua
    Wang, Yadong
    PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2018, : 1290 - 1295
  • [10] Binary particle swarm optimization with bit change mutation
    Lee, Sangwook
    Park, Haesun
    Jeon, Moongu
    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 2007, E90A (10) : 2253 - 2256